Unit 2 homework instructions
DKU Stats 101 Spring 2025 Session 3
Scoring guide
Content
- Getting the right answer is only a small part of the grade
- Good quality interpretation of your results is the name of the game
- If you see something that looks unusual in your data (outlier, some unusual distribution type) - investigate it!
- When explaining your results, say something interesting about them. Did it match your expectations? Why or why not?
- Brief explanations that simply repeat what I can visually see myself will not receive a good score
- On the other hand, filling the homework with pages of not very interesting description is not valuable either. The goal isn’t to write the most words, but find the most interesting things in the data.
- You do not need to be an expert in art for a good score, but I will expect you to look up basic information, such as “what does art typically sell for?” and “what is a standard size for paintings”? and so on to help you understand and set expectations your data.
- The information requested in the question prompts are only a starting point, if you find other interesting information along the way, please report that. You don’t need to look at the data forever but if there is obviously something else interesting in the data you should report it.
Technical
- Make sure your graphs are produced using
ggplot(), are well labeled, and are easy to read. - Make sure your tables (including regression tables) are produced with the
kable()function from theknitrpackage, are well labeled, and are easy to read. You can make your tables prettier with thekableExtrapackage. - Make sure you do not have anything rendered in your HTML file besides your results and, when asked for by a question, your code. That means no warnings, messages, or other output should appear in your final rendered HTML file.
- Convert your HTML file to PDF using the Microsoft Print to PDF option in the Print menu (PC) or the PDF button option from the Print menu (Mac)
- Make sure to accurately mark each page a question answer appears on when submitting on GradeScope.
- Delete the Scoring Guide section of the instructions before final rendering and submission.
Shanghai used house price analysis
Introduction
Question 1: Describing your data (10 points)
1a. Where is this data from?
For this dataset, describe the data according to the five Ws & how defined in the textbook Chapter 1.2. What are some possible problems with the who and what of the dataset?
The dataset you are using for this assignment is a subset of the original dataset that can be found here.
1b. What are the variable types?
For the following variables, please make a table.
One column should be the variable name, the second should be the variable type as defined in the textbook Chapter 1.3, and the third the units of the variable (if applicable). Note that you can find the units in the sh.house.raw.sample.hw2.csv file, though that dataset has different cases than the one you are given for your homework.
id标题链接居室数总面积居民楼总层数小区均价物业费用建造年份楼层分布区近地铁价格
Question 2: Association (20 points)
2a. Investigating average community price vs. green area: association
Using the Think-Show-Tell framework from the textbook (example on page 213), please examine the relationship in association terms between average price of a community and the percent of green area a community has. How strongly are they associated?
Note: for this question and all other Think sections in the homework, you do not need to report the W’s of the data (you have already completed this in Q1)
Think
Show
Tell
2b. Investigating average community price vs. year built
Using the Think-Show-Tell framework from the textbook, please examine the relationship in association terms between the average price for community and the year built. How strongly are they associated?
Think
Show
Tell
2c. Thinking about your results
Consider the results of 2a. and 2b. together. What can we understand about average community price in Shanghai? Why do you think you observe these results?
Question 3: Simple regression (20 points)
3a. Investigating price vs. area
Using the Think-Show-Tell framework from the textbook, please examine how the management fee of an apartment is related to the price of the apartment.
Think
Show
Tell
3b. Checking model fit
Make use of all the tools described in the textbook to assess model fit in the Think again section - if it is necessary to revise your model, do it in the Think again section. Then state any updated conclusions in the Revising conclusions section.
Think again
Revising conclusions
3c. Investigating price vs. apartment floor
Similar to 3a. and 3b., fully analyze the relationship between price and apartment floor.
Think
Show
Tell
Think again
Revising conclusions
3d. Thinking about your results
What can we learn about the determinants of apartment prices in Shanghai from these two investigations? Do the results surprise you? What lurking variables do you think could be at work here, if any?
Complete up to here for Homework Check - due January 26th at 11:59 pm.
Question 4: Multiple regression (30 points)
4a. Investigating area + floor and price
Using the Think-Show-Tell framework from the textbook, please examine how area and floor of an apartment are related to price. Make use of all the tools described in the textbook to assess model fit in the Think again section - if it is necessary to revise your model, do it in the Think again section. Then state any updated conclusions in the Revising conclusions section.
Think
Show
Tell
Think again
Revising conclusions
4b. Interpreting coefficients of 4a. model
Carefully interpret your coefficients from 4a. What do they mean? Are there any lurking variables here?
Think
Show
Tell
4c. Add the variable near a subway
Now add the variable near a subway to your model and analyze the relationship similar to what you did in 4a.
Think
Show
Tell
Think again
Revising conclusions
4d. Reinterpret your coefficients
Carefully interpret your coefficients from 4c. What do they mean? Any new lurking variables to consider?
Think
Show
Tell
4e. Thinking about your results
Consider the results of 4a.-4d. together. What can we learn about used housing prices in Shanghai? How did your conclusions change from 3d.? Why do you think they changed?
Question 5: Your own investigation (20 points)
5a. Selecting your own question
Develop your own model of used housing price in Shanghai. Use the Think-Show-Tell procedure to conduct your investigation. Think deeply about what your result means and interpret your coefficients carefully.
Think
Show
Tell
Think again
Revising conclusions
5b. In summary
Sum up everything that you have learned in this investigation. Do not simply repeat/rephrase your previous results but try to say something larger that synthesizes the results together to draw a more meaningful general conclusion.